Multiple georeferenced aerial image crop analysis and synthesis

ABSTRACT

Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

TECHNICAL FIELD

Embodiments described herein generally relate to aerial image analysis.

BACKGROUND

Precision agriculture seeks to improve farming management through theuse of image analysis. Precision agriculture may use satellite imageryto capture an image of a crop field or other large agricultural region.However, obtaining and analyzing satellite imagery is expensive,complex, and subject to various limitations. For example, satelliteimage quality is limited by the orbital distance from earth, satelliteimage frequency and location is limited by the satellite orbital period,and satellite image availability is limited by space weather ormeteorological events (e.g., clouds, storms). It is desirable to provideimproved imagery and analysis for precision agriculture.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an image synthesis method, in accordancewith at least one embodiment of the invention.

FIG. 2 is a block diagram of a mosaicked georeferenced image synthesismethod, in accordance with at least one embodiment of the invention.

FIG. 3 is a block diagram of a georeferenced image synthesis method, inaccordance with at least one embodiment of the invention.

FIG. 4 is a block diagram of a georeferenced image synthesis aerialvehicle, according to an example embodiment.

FIG. 5 is a block diagram illustrating a georeferenced image synthesissystem in an example form of an electronic device, according to anexample embodiment.

DESCRIPTION OF EMBODIMENTS

The present subject matter provides a technical solution for varioustechnical problems in providing improved imagery for aerial imagery,such as aerial imagery used in precision agriculture. Aerial imagery maybe captured from a manned or unmanned aerial vehicle (e.g., UAV,aircraft). The use of an aerial vehicle reduces or eliminates several ofthe challenges facing satellite imagery. For example, aerial vehicleimagery provides greater control over distance from target and time ofimage capture, and reduces or eliminates imagery interference caused byclouds or other obstacles. Similar advantages over satellite imagery maybe realized through the use of elevated, non-airborne imagery, such asimagery captured from vehicles, farm-mounted equipment, personaltracking devices, or other elevated imagery.

Aerial or elevated imagery may be analyzed to provide variousagricultural information, such as vegetative health, plant counts,population counts, plant presence estimation, weed presence, diseasepresence, chemical damage, standing water presence, wind damage,nutrient deficiency (e.g., nitrogen, phosphate, magnesium, potassium,water), or other agricultural information. The output analysis files maybe provided in the form of human viewable images (e.g., JPEG images) ofa geographic area, machine-readable geographic images (e.g., GeoTIFFfiles), or georeferenced data files (e.g., shapefiles). In addition toagricultural analysis, these systems and methods may be used to provideoutput analysis files useful in construction, infrastructure inspection,and other industries. While systems and methods are generally describedherein with respect to georeferenced aerial imagery, similar systems andmethods may be applied to any other georeferenced imagery, such aselevated imagery captured from farm-mounted georeferenced equipment orother mobile georeferenced-image capture devices.

The following description and the drawings sufficiently illustratespecific embodiments to enable those skilled in the art to understandthe specific embodiment. Other embodiments may incorporate structural,logical, electrical, process, and other changes. Portions and featuresof various embodiments may be included in, or substituted for, those ofother embodiments. Embodiments set forth in the claims encompass allavailable equivalents of those claims.

FIG. 1 is a block diagram of an image synthesis method 100, inaccordance with at least one embodiment of the invention. Method 100includes receiving multiple images 110 and 115 of an agricultural area(e.g., a crop field). Aerial vehicles generally capture agriculturalimagery at lower altitudes than satellites, and while this reduces oreliminates weather-based crop occlusion, aerial vehicle imagery usuallyrequires multiple images to cover a desired agricultural area. Each ofthe images 110 and 115 typically includes high resolution unprocessedimages (e.g., “raw” images) as output from an aerial image capturedevice. The raw images may be analyzed (e.g., processed) to provide ananalyzed output, such as a graphical representation of agriculturaldata. For example, image 110 may be analyzed to perform a plant count togenerate a first plant count analyzed image 120, and image 115 may besimilarly analyzed to generate a second plant count analyzed image 125.

Aerial or elevated imagery is often post-processed to account for aerialimage characteristics, such as aerial camera angle variations, aerialcamera altitude variations, topographic features (e.g., hills), or otheraerial image characteristics. To account for these imagecharacteristics, software may be used to project the images onto a mapplane or 3D surface, such as the rectangular agricultural map 140. Thesoftware may modify the images to improve the accuracy of the projectionof the image content, such as by scaling, warping, shifting,orthorectifying, or performing other image manipulation. Anorthorectification process may be applied to the first analyzed image120 and the second analyzed image 125 to form first orthorectified image130 and second orthorectified image 135. Once orthorectified,georeference data may be used to position and superimpose firstorthorectified image 130 and second orthorectified image 135 ontorectangular agricultural map 140. In an embodiment, rectangularagricultural map 140 may further include a default value (e.g., baselinevalue) for the region, such as a default number of plants per squaremeter.

Multiple aerial or elevated images may be combined to form a singleimage. The multiple images may be combined using image feature basedstitching, which stitches images together using software image featurerecognition. In image stitching, matching geographic regions may beidentified using various software-based feature recognition algorithms.For example, the first aerial image 110 and second aerial image 115 maybe combined, however combining raw image files often requirestransferring large image files and requires expensive and time-consumingfeature recognition software. In addition or alternative to imagestitching, georeference-based mosaicking may be used to process andcombine raw image files into a direct georeferenced mosaic image. Byusing georeference to perform mosaicking, the user can avoid thetransfer and processing of large raw image files used in imagestitching. For example, the first analyzed image 120 and the secondanalyzed image 125 may be positioned by direct georeferencing andsuperimposed onto a mosaicked image, such as onto rectangularagricultural map 140. In various embodiments, the data from the firstanalyzed image 120 and the second analyzed image 125 may be combinedinto another form of geographic information data file, such as aGeoTIFF, a raster file, a geographic vector file, or other type ofgeographic information data file.

In precision agriculture applications, processing raw image files toproduce a mosaicked agricultural information output file oftensignificantly reduces the file size and resulting image complexity. Forexample, the raw imagery may have a Ground Sample Distance (GSD) of 2cm, which corresponds to approximately 40 million pixels per acre. Ifeach pixel includes three values (e.g., red, green, blue), each acre ofraw imagery would correspond to approximately 120 million values. Thesesizeable raw images are then sent to an image stitching system, whichmay require significant transmission bandwidth, storage space, andprocessing power. In contrast to the use of raw images, mosaicking maybe used to reduce image file size before transmission. For example, if aparticular type of vegetative population count is used to determine thenumber of plants within each square meter, each acre may include up to4,047 plant count values. Various types of analysis per unit of area maybe used. Similarly, a source image may be divided into sub-regions of apredetermined pixel size or geographic area, and a multi-pixel analysis(e.g., pixel region analysis) may be used based on variousapproximations of number of plants in each sub-region, which may furtherreduce the size of the resultant images. A pixel-by-pixel analysis maybe performed on the raw image to provide an agricultural index, such asa normalized difference vegetation index (NDVI). Additional quantizationor data compression may be used to reduce the size of the outputmosaicked image. Further, because the output image already representsthe mosaicked combination of the raw images, no processor-intensivestitching is needed.

FIG. 2 is a block diagram of a mosaicked georeferenced image synthesismethod 200, in accordance with at least one embodiment of the invention.Aerial vehicles may be used to capture high resolution, unprocessed,“raw” agricultural images. The raw images may include a first raw image210, and may include a series of N overlapping images 215A-215N, wherethe overlapping images 215A-215N represent overlapping sections of anagricultural area. The raw images may be processed to provide vegetativehealth data or other agricultural data, generating a first analyzedimage 220 and a series of N overlapping images 225A-225N. The aerialvehicle may include one or more navigation sensors to providegeoreference data (e.g., latitude, longitude, altitude, cameraorientation). The georeference data may be associated with each of theraw images, and may be associated with each of the correspondinganalyzed images. Using the georeferenced data, the images may bemosaicked together to form an aggregated aerial image (e.g., a mosaic)230. The aggregated aerial image 230 may include one or more groups ofmosaicked analyzed images 235 superimposed on a larger image of theagricultural area. The larger image of the agricultural area may becaptured by the aerial vehicle or may be provided by an external source,and georeference information associated with the larger image may beused to determine the location of the superimposed mosaic of analyzedimages 235. While the mosaic of analyzed images 235 is shown as acontiguous block, the use of georeferenced captured images enablesgenerating an output without requiring the images to be overlapping. Forexample, first analyzed image 220 may be separated from a series of Noverlapping images 225A-225N within the group of mosaicked analyzedimages 235. This enables greater flexibility in capturing discontinuousregions, which may reduce time and costs associated with the aerialvehicle operation. Further, the use of georeferenced data in mosaickingreduces or eliminates the need for stitching multiple images using imageprocessing techniques. By reducing or eliminating the need forimage-feature-based stitching, the processing required to generate theoutput aggregated aerial image 230 is reduced significantly. Thesereductions in required processing enable a reduction in required powersource and processor performance, which may further reduce time andcosts associated with the aerial vehicle operation. In an embodiment,the reduced power and processor requirements may enable the aerialvehicle to perform image capture, processing, and georeferencemosaicking in real-time (e.g., while in flight). In an example, usingthe output aggregated aerial image 230 generated in-flight, a groundoperator (e.g., pilot or processor-based navigation system) may identifyareas of particular interest in real-time and command the aerial vehicleto perform additional analysis of that area.

FIG. 3 is a block diagram of a georeferenced image synthesis method 300,in accordance with at least one embodiment of the invention. Method 300includes receiving a plurality of raw aerial images 310. In variousembodiments, each image within the plurality of raw aerial images mayhave an associated geolocation, or sub-regions with each image may haverespective geolocations. Method 300 includes generating a plurality ofper-image processed geographic information data files 320. In anembodiment, the geographic information data file 320 includes a rasterdata file. Method 300 includes generating an aggregated geographicinformation data file 330 based on the plurality of per-image processedgeographic information data files and based on the associated pluralityof geolocations. In an embodiment, the aggregated geographic informationdata file 330 includes an aggregated raster data file.

Method 300 may generate one or more output files based on the aggregatedgeographic information data file. In an embodiment, method 300 includesgenerating a georeferenced image 340. The georeferenced image mayinclude a human-viewable image that includes geographic information,such as a GeoTIFF file. In an embodiment, method 300 includes generatinga shapefile 350. The shapefile may include data that can be used torender a viewable image, such as a vector representation of an area. Theshapefile may also include various attributes associated withsub-regions within the area. The generation of the shapefile 350 may bebased on the output of generating the aggregated geographic informationdata file 330, based on the output of generating the georeferenced image340, or based on a combination of the two. In an embodiment, method 300includes generating an aggregated aerial image 360. The aggregatedaerial image includes the plurality of processed aerial images arrangedrelative to each other based on the associated plurality ofgeolocations, such as the human-viewable aggregated aerial image 230shown in FIG. 2. In an embodiment, method 300 includes generating anoutput data file 370. The output data file 370 may include informationsimilar to or in addition to information included in the shapefile 350,the metadata included within the aggregated image 360, or otherinformation. In various embodiments, the output data file 370 mayinclude a file with one row per geographic location (e.g., one row perpixel) and data separated into multiple columns. For example, the datamay include separate columns for planters per acre, latitude, longitude,easting, northing, NDVI, or other data columns. The output data file 370may include human-readable data, such as a text file, a comma-separatedvalue (CSV) file, or another human-readable file. The output data file370 may include machine-readable data, such as a binary file formattedfor processing by machines.

FIG. 4 is a block diagram of a georeferenced image synthesis aerialvehicle system 400, according to an example embodiment. System 400includes an aerial vehicle 410 and a remote computing device 450. Aerialvehicle 410 includes an image capture device and location device tocapture raw aerial images 420 with associated georeference data. Aerialvehicle 410 also includes a processor that uses the georeferenced aerialimages 420 to generate corresponding analyzed georeferenced images 430.The processor then uses the georeference data to combine the analyzedgeoreferenced images 430 into an output mosaicked geospatial informationfile 440. The aerial vehicle 410 further includes a wirelesscommunication device that transmits the output mosaicked geospatialinformation file 440 to the remote computing device 450.

The use of an aerial vehicle 410 to generate and send the output file440 offers various advantages. By using direct georeferencing to combineanalyzed images instead of stitching multiple images, the processingtime and associated power may be reduced significantly. This furtherenables the use of mobile processors and lighter battery payloads, whichreduces costs and increases practical operational time for aerialvehicles. Further, instead of transferring high resolution source imagesneeded for stitching, the relatively small size of the output mosaickedgeospatial information file 440 enables a significantly reduced datatransfer requirement. In an embodiment, instead of transferringgigabytes of source image files, the output mosaicked geospatialinformation file 440 requires kilobytes of shapefile data, therebyreducing the required data throughput by several orders of magnitude.This enables real-time transmission and viewing of partial or completeoutput mosaicked geospatial information file 440, and enables acost-effective use of cellular modem transmission (e.g., LTE) on theaerial vehicle 410. In contrast, the gigabytes of source image filesrequired for stitching would be technically or financially impracticalor impossible using cellular modem transmission, and would still requirestitching to be performed on a remote device.

FIG. 5 is a block diagram illustrating a georeferenced image synthesissystem in an example form of an electronic device 500, within which aset or sequence of instructions may be executed to cause the machine toperform any one of the methodologies discussed herein, according to anexample embodiment. Electronic device 500 may represent vehicles used tocapture images, synthesize images, or both. In alternative embodiments,the electronic device 500 operates as a standalone device or may beconnected (e.g., networked) to other machines. For example, thecomponents of electronic device 500 may all be resident on an aerialvehicle, or may be distributed partially on an aerial vehicle andpartially on a remote computing device. In a networked deployment, theelectronic device 500 may operate in the capacity of either a server ora client machine in server-client network environments, or it may act asa peer machine in peer-to-peer (or distributed) network environments.The electronic device 500 may be implemented on a System-on-a-Chip(SoC), a System-in-a-Package (SiP), an integrated circuit (IC), aportable electronic device, a personal computer (PC), a tablet PC, ahybrid tablet, a personal digital assistant (PDA), a mobile telephone,or any electronic device 500 capable of executing instructions(sequential or otherwise) that specify actions to be taken by thatmachine to detect a user input. Further, while only a single electronicdevice 500 is illustrated, the terms “machine” or “electronic device”shall also be taken to include any collection of machines or devicesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein. Similarly, the term “processor-based system” shall be taken toinclude any set of one or more machines that are controlled by oroperated by a processor (e.g., a computer) to execute instructions,individually or jointly, to perform any one or more of the methodologiesdiscussed herein.

Example electronic device 500 includes at least one processor 502 (e.g.,a central processing unit (CPU), a graphics processing unit (GPU) orboth, processor cores, compute nodes, etc.), a main memory 504 and astatic memory 506, which communicate with each other via a link 508(e.g., bus). The main memory 504 or static memory 506 may be used tostore navigation data (e.g., predetermined waypoints) or payload data(e.g., stored captured images).

The electronic device 500 includes a navigation sensor 510. Navigationsensor 510 may include an IMU, which may include an accelerometer andgyroscope to output vehicle roll, pitch, yaw, acceleration, or otherinertial data. The navigation sensor 510 may include a compass toprovide heading, or may include a GNSS to provide location. Thenavigation sensor 510 may include a tightly coupled IMU and GNSS system.

The electronic device 500 may further include a display unit 512, wherethe display unit 512 may include a single component that provides auser-readable display and a protective layer, or another display type.The electronic device 500 may further include an input device 514, suchas a pushbutton, a keyboard, an NFC card reader, or a user interface(UI) navigation device (e.g., a mouse or touch-sensitive input). Theelectronic device 500 may additionally include a storage device 516,such as a drive unit. The electronic device 500 may additionally includea signal generation device 518 to provide audible or visual feedback,such as a speaker to provide an audible feedback or one or more LEDs toprovide a visual feedback. The electronic device 500 may additionallyinclude a network interface device 520, and one or more additionalsensors (not shown).

The storage device 516 includes a machine-readable medium 522 on whichis stored one or more sets of data structures and instructions 524(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 524 mayalso reside, completely or at least partially, within the main memory504, static memory 506, or within the processor 502 during executionthereof by the electronic device 500. The main memory 504, static memory506, and the processor 502 may also constitute machine-readable media.

While the machine-readable medium 522 is illustrated in an exampleembodiment to be a single medium, the term “machine-readable medium” mayinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more instructions 524. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present disclosure or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including but not limited to, by way ofexample, semiconductor memory devices (e.g., electrically programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM)) and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

The instructions 524 may further be transmitted or received over acommunications network 526 using a transmission medium via the networkinterface device 520 utilizing any one of a number of well-knowntransfer protocols (e.g., HTTP). Examples of communication networksinclude a local area network (LAN), a wide area network (WAN), theInternet, mobile telephone networks, and wireless data networks (e.g.,Wi-Fi, NFC, Bluetooth, Bluetooth LE, 3G, 5G LTE/LTE-A, WiMAX networks,etc.). The term “transmission medium” shall be taken to include anyintangible medium that is capable of storing, encoding, or carryinginstructions for execution by the machine, and includes digital oranalog communications signals or other intangible medium to facilitatecommunication of such software.

To better illustrate the method and apparatuses disclosed herein, anon-limiting list of embodiments is provided here.

Example 1 is a georeferenced image synthesis device comprising: an imagecapture device to capture a plurality of ground images, the plurality ofground images including an associated plurality of geolocations; and aprocessor to: generate a plurality of processed geospatial data filesbased on the plurality of ground images, the plurality of processedgeospatial data files identifying an information data set for each ofthe plurality of ground images; and generate an aggregated geospatialdata file based on the plurality of processed geospatial data files andon the associated plurality of geolocations, the aggregated geospatialdata file including geospatial information representing how theplurality of processed geospatial data files are geographically arrangedrelative to each other.

In Example 2, the subject matter of Example 1 optionally includeswherein: generating the plurality of processed geospatial data filesincludes generating a plurality of processed images; and generating theaggregated geospatial data file includes generating an aggregated imagebased on the plurality of processed images and on the associatedplurality of geolocations, the aggregated image including the pluralityof processed images arranged relative to each other based on theassociated plurality of geolocations.

In Example 3, the subject matter of any one or more of Examples 1-2optionally include wherein generating the aggregated geospatial datafile includes generating an aggregated geographic information data filebased on the plurality of processed images and on the associatedplurality of geolocations.

In Example 4, the subject matter of any one or more of Examples 1-3optionally include wherein generating the aggregated geospatial datafile includes arranging the plurality of processed images relative toeach other further based on a feature-based image stitching process.

In Example 5, the subject matter of any one or more of Examples 1-4optionally include wherein the plurality of ground images includes aplurality of aerial images captured by an aerial vehicle.

In Example 6, the subject matter of any one or more of Examples 1-5optionally include wherein the plurality of ground images includes aplurality of elevated images.

In Example 7, the subject matter of any one or more of Examples 1-6optionally include wherein generating the plurality of processed imagesis further based on a pixel-by-pixel analysis of the plurality of groundimages to identify the information data set for each of the plurality ofground images.

In Example 8, the subject matter of any one or more of Examples 1-7optionally include wherein generating the plurality of processed imagesis further based on a pixel regional analysis of the plurality of groundimages to identify the an information data set for each of the pluralityof ground images.

In Example 9, the subject matter of any one or more of Examples 1-8optionally include wherein the plurality of ground images furtherincludes at least one overlapping portion.

In Example 10, the subject matter of Example 9 optionally includeswherein generating an aggregated image is further based on the at leastone overlapping portion.

Example 11 is a georeferenced image synthesis method comprising:receiving a plurality of ground images, the plurality of ground imagesincluding an associated plurality of geolocations; generating aplurality of processed geospatial data files based on the plurality ofground images, the plurality of processed geospatial data filesidentifying an information data set for each of the plurality of groundimages; and generating an aggregated geospatial data file based on theplurality of processed geospatial data files and on the associatedplurality of geolocations, the aggregated geospatial data file includinggeospatial information representing how the plurality of processedgeospatial data files are geographically arranged relative to eachother.

In Example 12, the subject matter of Example 11 optionally includeswherein: generating the plurality of processed geospatial data filesincludes generating a plurality of processed images; and generating theaggregated geospatial data file includes generating an aggregated imagebased on the plurality of processed images and on the associatedplurality of geolocations, the aggregated image including the pluralityof processed images arranged relative to each other based on theassociated plurality of geolocations.

In Example 13, the subject matter of any one or more of Examples 11-12optionally include wherein generating the aggregated geospatial datafile includes generating an aggregated geographic information data filebased on the plurality of processed images and on the associatedplurality of geolocations.

In Example 14, the subject matter of any one or more of Examples 11-13optionally include wherein generating the aggregated geospatial datafile includes arranging the plurality of processed images relative toeach other further based on a feature-based image stitching process.

In Example 15, the subject matter of any one or more of Examples 11-14optionally include wherein the plurality of ground images includes aplurality of aerial images captured by an aerial vehicle.

In Example 16, the subject matter of any one or more of Examples 11-15optionally include wherein the plurality of ground images includes aplurality of elevated images.

In Example 17, the subject matter of any one or more of Examples 11-16optionally include wherein generating the plurality of processed imagesis further based on a pixel-by-pixel analysis of the plurality of groundimages to identify the information data set for each of the plurality ofground images.

In Example 18, the subject matter of any one or more of Examples 11-17optionally include wherein generating the plurality of processed imagesis further based on a pixel regional analysis of the plurality of groundimages to identify the information data set for each of the plurality ofground images.

In Example 19, the subject matter of any one or more of Examples 11-18optionally include wherein the plurality of ground images furtherincludes at least one overlapping portion.

In Example 20, the subject matter of Example 19 optionally includeswherein generating an aggregated image is further based on the at leastone overlapping portion.

Example 21 is at least one machine-readable medium includinginstructions, which when executed by a computing system, cause thecomputing system to perform any of the methods of Examples 11-20.

Example 22 is an apparatus comprising means for performing any of themethods of Examples 11-20.

Example 23 is at least one machine-readable storage medium, comprising aplurality of instructions that, responsive to being executed withprocessor circuitry of a computer-controlled device, cause thecomputer-controlled device to: receive a plurality of ground images, theplurality of ground images including an associated plurality ofgeolocations; generate a plurality of processed geospatial data filesbased on the plurality of ground images, the plurality of processedgeospatial data files identifying an information data set for each ofthe plurality of ground images; and generate an aggregated geospatialdata file based on the plurality of processed geospatial data files andon the associated plurality of geolocations, the aggregated geospatialdata file including geospatial information representing how theplurality of processed geospatial data files are geographically arrangedrelative to each other.

Example 24 is a georeferenced image synthesis apparatus comprising:means for receiving a plurality of ground images, the plurality ofground images including an associated plurality of geolocations; meansfor generating a plurality of processed geospatial data files based onthe plurality of ground images, the plurality of processed geospatialdata files identifying an information data set for each of the pluralityof ground images; and means for generating an aggregated geospatial datafile based on the plurality of processed geospatial data files and onthe associated plurality of geolocations, the aggregated geospatial datafile including geospatial information representing how the plurality ofprocessed geospatial data files are geographically arranged relative toeach other.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” Such examples can include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to allowthe reader to quickly ascertain the nature of the technical disclosure.It is submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. In the aboveDetailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment, and it is contemplated that suchembodiments can be combined with each other in various combinations orpermutations. The scope of the invention should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A georeferenced image synthesis devicecomprising: an image capture device to capture a plurality of groundimages, the plurality of ground images including an associated pluralityof geolocations; and a processor to: generate a plurality of processedgeospatial data files based on a pixel regional analysis the pluralityof ground images and the plurality of geolocations, the plurality ofprocessed geospatial data files identifying an information data set foreach of the plurality of ground images, the information data setincluding a plurality of data values, each of the plurality of datavalues associated with a respective sub-region of each of the pluralityof ground images; and generate an aggregated geospatial data file basedon the plurality of processed geospatial data files and on theassociated plurality of geolocations, the aggregated geospatial datafile including geospatial information representing how the plurality ofprocessed geospatial data files are geographically arranged relative toeach other.
 2. The device of claim 1, wherein: generating the pluralityof processed geospatial data files includes generating a plurality ofprocessed images; and generating the aggregated geospatial data fileincludes generating an aggregated image based on the plurality ofprocessed images and on the associated plurality of geolocations, theaggregated image including the plurality of processed images arrangedrelative to each other based on the associated plurality ofgeolocations.
 3. The device of claim 1, wherein generating theaggregated geospatial data file includes generating an aggregatedgeographic information data file based on the plurality of processedimages and on the associated plurality of geolocations.
 4. The device ofclaim 1, wherein generating the aggregated geospatial data file includesarranging the plurality of processed images relative to each otherfurther based on a feature-based image stitching process.
 5. The deviceof claim 1, wherein the plurality of ground images includes a pluralityof aerial images captured by an aerial vehicle.
 6. The device of claim1, wherein the plurality of ground images includes a plurality ofelevated images.
 7. The device of claim 1, wherein generating theplurality of processed images is further based on a pixel-by-pixelanalysis of the plurality of ground images to identify the informationdata set for each of the plurality of ground images.
 8. The device ofclaim 1, wherein the information data set for each of the plurality ofground images includes at least one of a vegetation index, a vegetativehealth, a plant count, a population count, a plant presence, a weedpresence, a disease presence, a chemical damage, a wind damage, astanding water presence, and a nutrient deficiency.
 9. The device ofclaim 1, wherein the plurality of ground images further includes atleast one overlapping portion.
 10. The device of claim 9, whereingenerating an aggregated image is further based on the at least oneoverlapping portion.
 11. A georeferenced image synthesis methodcomprising: receiving a plurality of ground images, the plurality ofground images including an associated plurality of geolocations;generating a plurality of processed geospatial data files based on apixel regional analysis the plurality of ground images and the pluralityof geolocations, the plurality of processed geospatial data filesidentifying an information data set for each of the plurality of groundimages, the information data set including a plurality of data values,each of the plurality of data values associated with a respectivesub-region of each of the plurality of ground images; and generating anaggregated geospatial data file based on the plurality of processedgeospatial data files and on the associated plurality of geolocations,the aggregated geospatial data file including geospatial informationrepresenting how the plurality of processed geospatial data files aregeographically arranged relative to each other.
 12. The method of claim11, wherein: generating the plurality of processed geospatial data filesincludes generating a plurality of processed images; and generating theaggregated geospatial data file includes generating an aggregated imagebased on the plurality of processed images and on the associatedplurality of geolocations, the aggregated image including the pluralityof processed images arranged relative to each other based on theassociated plurality of geolocations.
 13. The method of claim 11,wherein generating the aggregated geospatial data file includesgenerating an aggregated geographic information data file based on theplurality of processed images and on the associated plurality ofgeolocations.
 14. The method of claim 11, wherein generating theaggregated geospatial data file includes arranging the plurality ofprocessed images relative to each other further based on a feature-basedimage stitching process.
 15. The method of claim 11, wherein theplurality of ground images includes a plurality of aerial imagescaptured by an aerial vehicle.
 16. The method of claim 11, wherein theplurality of ground images includes a plurality of elevated images. 17.The method of claim 11, wherein generating the plurality of processedimages is further based on a pixel-by-pixel analysis of the plurality ofground images to identify the information data set for each of theplurality of ground images.
 18. The method of claim 11, wherein theinformation data set for each of the plurality of ground images includesat least one of a vegetation index, a vegetative health, a plant count,a population count, a plant presence, a weed presence, a diseasepresence, a chemical damage, a wind damage, a standing water presence,and a nutrient deficiency.
 19. The method of claim 11, wherein theplurality of ground images further includes at least one overlappingportion.
 20. The method of claim 19, wherein generating an aggregatedimage is further based on the at least one overlapping portion.